Journal article
Automated segmentation of choroidal layers from 3-dimensional macular optical coherence tomography scans
Journal of neuroscience methods, Vol.360, pp.109267-109267
08/01/2021
DOI: 10.1016/j.jneumeth.2021.109267
PMID: 34157370
Abstract
Changes in choroidal thickness are associated with various ocular diseases, and the choroid can be imaged using spectral-domain optical coherence tomography (SD-OCT) and enhanced depth imaging OCT (EDI-OCT).
Eighty macular SD-OCT volumes from 80 patients were obtained using the Zeiss Cirrus machine. Eleven additional control subjects had two Cirrus scans done in one visit along with enhanced depth imaging (EDI-OCT) using the Heidelberg Spectralis machine. To automatically segment choroidal layers from the OCT volumes, our graph-theoretic approach was utilized. The segmentation results were compared with reference standards from two independent graders, and the accuracy of automated segmentation was calculated using unsigned/signed border positioning/thickness errors and Dice similarity coefficient (DSC). The repeatability and reproducibility of our choroidal thicknesses were determined by intraclass correlation coefficient (ICC), coefficient of variation (CV), and repeatability coefficient (RC).
The mean unsigned/signed border positioning errors for the choroidal inner and outer surfaces are 3.39 ± 1.26 µm (mean ± standard deviation)/− 1.52 ± 1.63 µm and 16.09 ± 6.21 µm/4.73 ± 9.53 µm, respectively. The mean unsigned/signed choroidal thickness errors are 16.54 ± 6.47 µm/6.25 ± 9.91 µm, and the mean DSC is 0.949 ± 0.025. The ICC (95% confidence interval), CV, RC values are 0.991 (0.977–0.997), 2.48%, 14.25 µm for the repeatability and 0.991 (0.977–0.997), 2.49%, 14.30 µm for the reproducibility studies, respectively.
The proposed method outperformed our previous method using choroidal vessel segmentation and inter-grader variability.
This automated segmentation method can reliably measure choroidal thickness using different OCT platforms.
•Novel, memory-efficient, 3D automated choroidal layer segmentation method for OCTs.•Multiscale segmentation from macular scans using LOGIMOS method.•Works on Cirrus and Heidelberg machines (good results observed with images from other OCT machines).•High reliability/reproducibility demonstrates significant superiority to previous method.•Could be useful clinically for analysis and monitoring of choroidal diseases, e.g. AMD.
Details
- Title: Subtitle
- Automated segmentation of choroidal layers from 3-dimensional macular optical coherence tomography scans
- Creators
- Kyungmoo Lee - University of IowaAlexis K Warren - University of Iowa Hospitals and ClinicsMichael D Abràmoff - University of Iowa Hospitals and ClinicsAndreas Wahle - University of IowaS. Scott Whitmore - University of Iowa Hospitals and ClinicsIan C Han - University of Iowa Hospitals and ClinicsJohn H Fingert - University of Iowa Hospitals and ClinicsTodd E Scheetz - University of Iowa Hospitals and ClinicsRobert F Mullins - University of Iowa Hospitals and ClinicsMilan Sonka - University of Iowa Hospitals and ClinicsElliott H Sohn - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of neuroscience methods, Vol.360, pp.109267-109267
- DOI
- 10.1016/j.jneumeth.2021.109267
- PMID
- 34157370
- NLM abbreviation
- J Neurosci Methods
- ISSN
- 0165-0270
- eISSN
- 1872-678X
- Publisher
- Elsevier B.V
- Grant note
- DOI: 10.13039/100000002, name: National Institutes of Health, award: P30-EY025580, R01-EB004640, R01-EY026547; DOI: 10.13039/100000053, name: NEI; DOI: 10.13039/100001818, name: Research to Prevent Blindness
- Language
- English
- Date published
- 08/01/2021
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984186703602771
Metrics
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